all points in p not lying at infinity (p[i][2]!=0) are assumed to be corners of very good quality, in the region around these points no new features are detected, leave empty if you just want to detect some features in the image In any case p is cleared and only new detected points are added before returning. Example: assume MaxNum=100, you pass a vector p with 40 points of which 12 have the third coordinate w=0. You will get back a vector of maximum size 72, it may be smaller if Distance and MinCornerness have too large values

quality

after returning, for each point in p there is a quality in quality, no input requirements

all points in p not lying at infinity (p[i][2]!=0) are assumed to be corners of very good quality, in the region around these points no new features are detected, leave empty if you just want to detect some features in the image. In any case p is cleared and only new detected points are added before returning. Example: assume MaxNum=100, you pass a vector p with 40 points of which 12 have the third coordinate w=0. You will get back a vector of maximum size 72, it may be smaller if Distance and MinCornerness have too large values

quality

after returning, for each point in p there is a quality in quality, no input requirements

all points in p not lying at infinity (p[i][2]!=0) are assumed to be corners of very good quality, in the region around these points no new features are detected, leave empty if you just want to detect some features in the image In any case p is cleared and only new detected points are added before returning. Example: assume MaxNum=100, you pass a vector p with 40 points of which 12 have the third coordinate w=0. You will get back a vector of maximum size 72, it may be smaller if Distance and MinCornerness have too large values

quality

after returning, for each point in p there is a quality in quality, no input requirements